| 多元系统耦合多新息随机梯度类辨识方法 |
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| 引用本文： | 丁锋,汪菲菲.多元系统耦合多新息随机梯度类辨识方法[J].南京气象学院学报,2014,6(1):1-16. |
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| 作者姓名： | 丁锋 汪菲菲 |
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| 作者单位： | 江南大学 物联网工程学院,无锡,214122;江南大学 控制科学与工程研究中心,无锡,214122;江南大学 教育部轻工过程先进控制重点实验室,无锡,214122;江南大学 物联网工程学院,无锡,214122 |
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| 基金项目： | 国家自然科学基金(61273194);江苏省自然科学基金(BK2012549);高等学校学科创新引智"111计划"(B12018) |
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| 摘 要： | 针对多元线性回归系统,利用耦合辨识概念和多新息辨识理论,讨论了多元随机梯度算法、多元多新息随机梯度算法,以及变递推间隔多元多新息梯度算法,进一步分解多元系统为一些子系统,给出了耦合子系统随机梯度算法、耦合随机梯度算法、耦合子系统多新息随机梯度算法、耦合多新息随机梯度算法,并将这些方法推广到多元伪线性滑动平均系统和多元伪线性自回归滑动平均系统.文中给出了几个典型耦合随机梯度算法、耦合多新息随机梯度算法的计算步骤和示意图.
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| 关 键 词： | 参数估计 递推辨识 梯度搜索 最小二乘 辅助模型辨识思想 多新息辨识理论 递阶辨识原理 耦合辨识概念 多元系统 |
| 收稿时间： | 2014/1/6 0:00:00 |
Coupled multi-innovation stochastic gradient type identification methods for multivariate systems |
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| DING Feng and WANG Feifei.Coupled multi-innovation stochastic gradient type identification methods for multivariate systems[J].Journal of Nanjing Institute of Meteorology,2014,6(1):1-16. |
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| Authors: | DING Feng and WANG Feifei |
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| Institution: | School of Internet of Things Engineering, Jiangnan University, Wuxi 214122;Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122;School of Internet of Things Engineering, Jiangnan University, Wuxi 214122 |
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| Abstract: | For multivariate linear regression systems,using the coupling identification concept and the multi-innovation identification theory,this paper discusses a multivariate stochastic gradient algorithm,a multivariate multi-innovation stochastic gradient algorithm,and an interval-varying multivariate multi-innovation gradient algorithm,decomposes a multivariate system into several subsystems,and presents a coupled subsystem stochastic gradient algorithm,a coupled stochastic gradient algorithm,a coupled subsystems multi-innovation stochastic gradient algorithm and a coupled multi-innovation stochastic gradient algorithm.These methods are extended to multivariate pseudo-linear moving average systems and multivariate pseudo-linear autoregressive moving average systems.Finally,this paper gives the steps and diagrams for computing the parameter estimates using several typical coupled stochastic gradient algorithms and coupled multi-innovation stochastic gradient algorithms. |
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| Keywords: | parameter estimation recursive identification gradient search least squares auxiliary model identification idea multi-innovation identification theory hierarchical identification principle coupling identification concept multivariate system |
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